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Geographic Information Systems

GIS Analysis Capabilities

Much of the initial time and expense in operating a GIS are in developing the various data layers. Once these data layers are in the system, much time is often spent producing maps of the various data layers. These maps are useful but do not represent the real power of the GIS. The unique power of the GIS is in the analysis that it can perform. Unfortunately, because so much effort is devoted to developing the database layers, often the main purpose for developing the GIS (providing aids to the decision-making process) is neglected. It is the analytical functions applied to the spatial and nonspatial attribute data of the GIS that provide the power to aid in the decision-making process.

The analysis capabilities of the GIS provide the answers to the five basic questions defined by Walker and Miller (1990). These five basic questions, which were discussed previously, should be reviewed before the analytical capabilities of GISs are studied.

Aronoff (1989) described four major GIS analysis capabilities: (1) maintenance and analysis of spatial data, (2) maintenance and analysis of attribute data, (3) integrated analysis of spatial and attribute data, and (4) output formatting. The first two functions deal with maintaining the data and the fourth function is for producing output from the system. The real power of the GIS is its ability to integrate the analysis of spatial and attribute data. Spatial data can be stored as raster or vector structures. Some analytical functions can work equally well with raster or vector data, some analytical functions will perform better with raster data, and some functions will perform better with vector data. GIS systems of the future will have improved user interfaces and expert systems to advise the user on how to utilize the existing database and software to obtain the desired resource information (Coulson et al. 1987, Goodenough et al. 1987, McKeown 1987).

Maintenance and Analysis of the Spatial Data

Maintenance and analysis capabilities of the GIS will permit transforming spatial data files, editing spatial data, and assessing the quality and accuracy of the data. Aronoff (1989) described seven maintenance and analysis functions for spatial data: format transformation, geometric transformation, transformation between map projections, conflation, edge matching, editing of graphic elements, and line coordinate thinning.

Because data for the GIS may come in many formats (e.g., DLG, TIGER, MOSS, GRASS, raster run-lengths encoded, and DIME), GISs must be able to transform data among various formats. Conversions from various formats and from raster to vector and vector to raster are examples of format transformation. Because of inaccuracies in source maps, slight differences in map projection, or digitizing errors, data layers may not be precisely registered to a common coordinate system or a standard data layer. Geometric transformations are procedures used to ensure that each data layer precisely overlays the other data layers. Some data may be supplied in geometric coordinates (latitude and longitude), whereas other data may be supplied in UTM or state plane coordinates. Functions to transform from one map projection to another are required to transform all sources of data to a common map projection.

Conflation functions are used to ensure that common boundaries between different data layers share the exact coordinates. For example, the edge of one end of a wetland is adjacent to a gravel road, and the gravel road is the boundary on a wildlife management area. The location of the wetland was digitized for the wetland data layer, the road was digitized for the transportation data layer, and the wildlife management area was digitized for the public lands data layer. When all three layers were plotted as one map, the common boundaries of the wetlands, road, and wildlife management area are similar, but not exactly the same. Conflation procedures ensure that common boundaries are defined with exactly the same coordinates.

Usually all features on one map sheet will be digitized and then all features on the adjacent map will be digitized. Because of slight error in the maps and imprecisions in the digitizing process, the boundary of a feature crossing onto the adjacent map may not match precisely. Edge-matching functions ensure that the edge of a feature on one map perfectly matches the feature edge on the adjacent map.

GISs should have numerous editing functions to aid in adding, deleting, and changing the geographic positions of features. In the digitizing process, more coordinates are collected for a line than are actually required to represent the line. Line-coordinate thinning processes can eliminate coordinates that are not essential to represent the line and can greatly reduce the disk space required to store the coordinates needed to represent the line.

Maintenance and Analysis of Nonspatial Attribute Data

Just as various functions are required to edit, convert, and maintain spatial data, functions for editing, converting, and maintaining the nonspatial attribute data also are required. Attribute editing functions should allow for retrieving, editing, and changing the attributes. Attribute query functions allow records in the attribute databases to be selected. Using the attribute data query functions on the attribute database for the soils layer would allow one to select all soils that are sandy and have a "T" value >4 (T values represent the tons of topsoil likely to be eroded per 0.4 ha/year). Attribute query functions should support the use of complex Boolean expressions on the attributes of one or more data layers.

Integrated Analysis of Spatial and Attribute Data

The ability to effectively process spatial and attribute data is what primarily distinguishes GISs from automated mapping and computer-aided drafting systems (Aronoff 1989). The integrated analysis of spatial and attribute data can be divided into four categories (Berry 1987, Aronoff 1989): retrieval/classification/measurement, overlay, neighborhoods, and connectivity of network functions.

The retrieval processes are used to answer the first three basic questions that a GIS can address (Walker and Miller 1990): what exists at a particular location; where are certain conditions met; and what changes have occurred and where have changes occurred over time? Retrieval processes operate on spatial and attribute data. Output of the selective searches can create new layers in the database, tabular reports, interactive displays, or maps.

Classification processes frequently are performed after a retrieval process and are used to assign a new attribute. If deciduous, coniferous, and mixed forest types are retrieved from a vegetational data layer, a classification function could be used to assign a new attribute, forests, to the areas retrieved. Classification functions can be applied on one layer or on multiple data layers.

Measure functions calculate distances between points, lengths of lines, perimeters and areas of polygons, and sizes of continuous areas of the same feature. Selecting all wetlands >8 ha in size from a data layer describing the various wetlands types would require a retrieval process (retrieve all wetlands types), a classification process (classify the various retrieved wetlands types as one category), a measurement process (determine the size of all continuous area of wetlands), and a final retrieval process (select all wetlands >8 ha).

Overlay operators are some of the most fundamental and most frequently used processes in GIS applications. Aronoff (1989) described two types of overlay operators-arithmetic and logical. Arithmetic operators are used to add, subtract, divide, or multiply values in one data layer by a constant or by values in another data layer in corresponding location (Fig. 13). Logical overlays are used to identify areas where one feature or condition exists in one data layer and another feature or condition exists in another data layer. The logical overlays can use various logical Boolean operators such as "and" where both condition A and condition B are met, "or" where condition A or condition B is met, and "and not" to idenffy areas where condition A is met, but not condition B (Fig. 14).

GIF -- Figure 13
GIF -- Figure 14
Fig. 13. -- An arithmetic overlay operation is used to add the value in data layer A to the value in data layer B. The sum of data layers A and B is stored in the output data layer (Aronoff 1989). Fig. 14. -- Logical overlay operation of area A in data layer 1 with area B in data layer 2 can have many results. The resultat area C contains neither A nor B, area D contains A and B, areas H and F contain A but not B, and areas E and G contain B but not A (Aronoff 1989).

Neighborhood processes evaluate the characteristics of the area surrounding a specific location (Aronoff 1989). Measuring the length of edge between habitat types within 2 km of the location of a radio-telemetered animal is an example of a neighborhood function. All neighborhood processes require three parameters: the location of one or more target areas, the size of the neighborhood surrounding the targeted area, and a function to perform in the defined neighborhood and assigned to the targeted area. Five numerical functions can be applied in the neighborhood: average, diversity, majority, maximum/minimum, and total (Aronoff 1989).

Point-in-polygon and line-in-polygon operations are fundamental neighborhood process for vector-based GISs (Aronoff 1989). A point-in-polygon function is used to determine which features (as defined by polygons) cover a specific point. In analysis of radiotelemetry data, point-in-polygon routines are used in vector-based GISs to ascertain various habitat parameters occurring at each location made for the animal being studied. In a similar manner, line-in-polygon routines are used to ascertain which polygon contains a specified line.

Various topographic functions can be computed from elevation information provided by DEMs or DTMs. Topographic functions usually are computed from raster data of elevation. The elevation for a particular cell (X), plus the elevation of the eight orthogonal (O) and diagonal (D) neighbors (Fig. 15), can be used to ascertain slope, aspect, and topographic position (ridge, valley, knoll). These topographic parameters frequently are highly correlated with the distribution of plant and animal species, and they frequently can be used in remote sensing applications to distinguish spectrally similar habitats. For example, spectrally, coastal dunes often cannot be separated from sandy flats. Slope and topography position parameters can readily separate these two spectrally similar sandy habitats.

GIF -- Figure 15
Fig. 15. -- A cell can have orthogonal and diagonal neighbors. Orthogonal neighbors for cell "X" are labeled with an "O", and diagonal neighbors are labeled by a "D." Some neighborhood operations recognize only orthogonal neighbors, whereas other neighborhood operations process orthogonal or diagonal neighbors.

Other topography functions include illumination techniques to enhance display of elevation or relief, viewshed modeling to determine what areas can be seen from a specific point for appraising visual impacts, watershed analysis to determine the extent of the watershed, and perspective views to produce illustrations of the relief as viewed from a given point. These topography functions usually are classified as connectivity functions. Various other neighborhood functions are available for interpolating information from various point locations.

Connectivity functions accumulate values over a connected area (Aronoff 1989). Connectivity functions require (1) a procedure for connecting the areas and (2) a measure for the connected area. Continuity measures are connectivity functions that typically measure the size of a continuous area. Evaluating habitat for a particular avian species using bottomland hardwood forests will require the locations of all bottomland hardwoods. However, if the species is known to use only bottomland hardwood forests that are at least 50 ha in size, a continuity measure would be used to ascertain the size of each continuous block of bottomland hardwood forests. Continuity measures are a valuable tool for measuring habitat fragmentation.

Proximity to a habitat type also may be valuable in evaluating the availability of habitat to certain species. Proximity analysis is the measure of distance between features. Figure 16 illustrates the use of proximity function to define the area within 250 m of all streams.

GIF -- Figure 16
Fig. 16. -- A proximity function was used to define all areas within 250 m of streams.

Various other connectivity functions are used in GIS applications. Network functions are processes to optimize vehicle routing and to divide areas into service districts for optimizing limited resources (Aronoff 1989). Spread functions can be used to evaluate the cost of transversing an area and often are used to evaluate alternative routes for transportation and utilities. Such routines will find the lowest cost route between two points. Cost can be measured separately for economic, social, or environmental cost, or total costs can be calculated by summing the weighted economic, social, and environmental costs.

Cartographic Modeling

The previous sections described many of the processes of a GIS. Tomlin and Berry (1979) and Berry (1987) developed the term "cartographic modeling" to describe the use of basic GIS processes in a logical sequence to solve complex spatial problems. A series of standard processes, such as reclassifying, overlaying, distance, and neighborhood operations, is used to subdivide the spatial problems into a series of primitive operations. A cartographic model can be depicted with a flowchart. The initial data layers are shown, followed by a standard process performed on the data layer, followed by an output (often a data layer) created by the processing function performed. An output data layer then may be used as input to another process. Various inputs, processes, and outputs are diagrammed until the solution is derived. The cartographic model provides the pathway for solving the specified spatial problem. Prior to implementing the cartographic model with the GIS, the flowchart of the cartographic model can be reviewed and easily revised. Cartographic models are an excellent means to help determine which data layers will be required to solve any spatial problem.

Berry (1987) identified many advantages of the use of cartographic models. These models are capable of dynamic simulations and provide spatial "what if" analysis. Koeln (1980) used a GIS to identify the "best" (or least bad) sites for general aviation airports. Three different sets of weights for economic, social, and environmental impacts were used to simulate the attitude of three different groups of decision makers. One set of simulated decision makers (the misers) was concerned primarily with the economic costs but recognized some social and environmental costs. Another set of simulated decision makers (the environmentalists) weighted the environmental costs of general aviation airport the highest, followed by social costs, then economic costs. The altruist group of decision makers weighted economic, social, and environmental costs nearly equally. Through the cartographic modeling approach, three different sets of "best sites" for general aviation airports were selected based upon weights assigned by the simulated decision makers. Because the choice of location for general aviation airports is quite constrained, many of the best sites as determined from the misers' weights were identical to the best sites chosen with the weights of the environmentalists and altruists.

Another advantage of the cartographic modeling approach is its flexibility. New considerations can be added easily or existing ones can be refined. The cartographic modeling approach also provides an effective means for communicating the process used, including consideration of the specific application and fundamental procedures applied. A flowchart provides an excellent tool for communicating the logic, assumptions, and relationships used in the analysis.

Output Processing

Information from GISs must be reported. The reports can be statistical tabulations, such as reports of various habitat parameters for a study area or the polygon defining the home range of an animal, interactive displays of data on color and monochrome monitors, and hard-copy maps. Both hard-copy and soft-copy (images on a color or monochrome monitor) are essential. The reporting capabilities of GISs vary greatly, depending upon the software used. Modem GISs have interactive capabilities to display images on color or monochrome monitors. Most modem GISs have improved, and are improving, hardcopy output capabilities. Some systems have map automation procedures, text-labeling abilities, and graphic capabilities that nearly match the map-making capabilities of automated mapping and drafting systems. The reduction in the cost of image display devices, color plotters, and film recorders allows many GIS users to afford quality output devices. The expansion of software output capabilities seen for most commercially available GISs reflects the availability of an increasing array of quality output devices.

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